A break in travel coincided with an invitation to the sea. We visit the same island on most holidays and I am lucky to sneak away when I need some downtime. For me this means long early morning running on the beach--typically in the darkness but at this time of year--mostly all alone.

This morning I queued up one of my favorite podcasts. The New York Public Library or NYPL podcast.

What do Mark Zuckerberg and Martin Luther have in common? More than you think, says historian and political commentator Niall Ferguson in his new book. Ferguson was at The New York Public Library to speak with Gillian Tett, U.S. Managing Editor of the Financial Times, about the power and limitations of networks throughout history.

"The public sphere has structurally changed in such a way that Facebook has become the biggest and most powerful content publisher in history," says Ferguson, "and yet Facebook is regulated as if it were just a tech company with no liability for the content that appears on the site."
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Fascinating to hear a true historic perspective on networks using the discovery of the printing press and how hierarchal networks have persisted. For instance the title differentiates between us in the town square and the tower of the royals or elite. We may think that we have large networks in our technological advanced age but what if I told you the towers now belong to Facebook, Twitter, Google, etc? We are still running around in the square serving the interests of Mark Zuckerberg or the like.

Think about the members of your social media groups. It isn't a homogeneous group. There is variety in the number of edges, nodes, and distance between them. Maybe in your connections you can identify the highest value connections defining value as perhaps a job lead, and introduction, or even a path to a new source of content.

We might think of social networks as a modern invention but think about distribution of information once the printing press was revolutionized. The reformation would not have been possible without the innovation of Gutenberg's printing press and the rise of early networks. You are welcome Martin Luther.

This conversation made me curious about my own network. You can visualize your LinkedIn network on Socilab for free using their open source project. I eliminated the names for privacy but the visual is extremely compelling--or is it just me that thinks this is cool. The interesting part is the dynamic flow so go discover yours--I'll wait right here.

Here is a dense version of my network as well. I can't tell if the shape is because the LinkedIn API limits the number of connections you can map (500) or if this is the actual depiction of the network.

I collected some data summarized below because I can't really make sense of my eyeball-ish shaped network.

Although I run my own company it is interesting to see how I can play a bigger role in connecting the members of my network.

The measures make sense as I work between a variety of disciplines and this is reflected in effective size and betweenness.

​I hope you can see how perhaps we shouldn't strive for more connections and followers--It is more important to create the right ones.

Absolute size: 94.39% This metric isn't that useful in my opinion. Here are a few statistics that measure the quality of your connections

Effective Size: 99.18% This is similar to the count distinct function--the utility of a large network evaluates the value if different connects in your network. It measures unique clusters.

Network constraint 93.20% I thought I failed this one until I discovered the lower your raw score--the more open and dispersed your network is--less shouting in a black hole--closer to "0" the better. I was .20

Density: 85.14% If you have a fairly large network but fewer nodes--this can be an area to develop. Its on my list

Hierarchy: 85.14 % If the majority of your contacts come from bosses for example--It can be a handicap. I score decent on hierarchy meaning there are many peer to peer relationships mixed in.

​Betweenness: 99.01% The raw score here is quite large demonstrating I have a short distance to many nodes and a lot of opportunity to move information within my network.